Dummy-coded maps for cue encoding

clear all;

close all;

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0013.nii'));

spm('Defaults','fMRI')

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel/sub-0014/con_0013.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 29951100 bytes Loading image number: 75 Elapsed time is 14.550953 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 7164195 Bit rate: 22.77 bits

contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...

'stim_P', 'stim_V', 'stim_C', 'stim_G',...

'stimXexpect_P', 'stimXexpect_V', 'stimXexpect_C',...

'motor', ...

'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...

'simple_stim_P', 'simple_stim_V', 'simple_stim_C',...

'simple_stimXexpect_P', 'simple_stimXexpect_V', 'simple_stimXexpect_C'};

m = mean(con_data_obj);e

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans'); % display

SPM12: spm_check_registration (v7759) 19:08:37 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 33.33% Expected 3.75 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 48 Uncorrected: 8 images Cases 20 23 45 46 48 59 72 74 Retained 10 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 41.33% Expected 3.75 outside 95% ellipsoid, found 6 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 2 Uncorrected: 6 images Cases 2 32 41 43 67 73 Mahalanobis (cov and corr, q<0.05 corrected): 2 images Outlier_count Percentage _____________ __________ global_mean 0 0 global_mean_to_variance 1 1.3333 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 0 0 mahal_cov_uncor 8 10.667 mahal_cov_corrected 1 1.3333 mahal_corr_uncor 6 8 mahal_corr_corrected 1 1.3333 Overall_uncorrected 14 18.667 Overall_corrected 2 2.6667

SPM12: spm_check_registration (v7759) 19:09:09 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 75

%for s = 1:length(wh_outlier_corr)

%disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 2 participants, size is now 73

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are outliers:... sub-0015" "participants that are outliers:... sub-0091"

disp(n);

{'sub-0015'} {'sub-0091'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 19:09:14 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.036754 Image 1 33 contig. clusters, sizes 1 to 73005 Positive effect: 63739 voxels, min p-value: 0.00000000 Negative effect: 9657 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 19:09:16 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .001, 'fdr');

Image 1 FDR q < 0.001 threshold is 0.000564 Image 1 30 contig. clusters, sizes 1 to 55909 Positive effect: 51321 voxels, min p-value: 0.00000000 Negative effect: 5036 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 19:09:18 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 1301 voxels displayed, 55056 not displayed on these slices

sagittal montage: 1390 voxels displayed, 54967 not displayed on these slices

sagittal montage: 1331 voxels displayed, 55026 not displayed on these slices

axial montage: 10315 voxels displayed, 46042 not displayed on these slices

axial montage: 11490 voxels displayed, 44867 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _______________ __________ ________________ -0.3063 {'anxiety' } 0.43533 {'visual' } -0.29236 {'trait' } 0.38069 {'object' } -0.28382 {'disorder' } 0.36996 {'objects' } -0.28093 {'age' } 0.33332 {'attention' } -0.27561 {'rating' } 0.31964 {'solving' } -0.27473 {'pain' } 0.30946 {'calculation' } -0.27331 {'sensation' } 0.30224 {'rotation' } -0.26541 {'affect' } 0.30216 {'shape' } -0.26252 {'stimulation'} 0.29884 {'orthographic'} -0.2606 {'affective' } 0.28124 {'numerical' }

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(imgs2, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.1606 -25.4783 0.0000 1.0000 Cog Wholebrain 0.0307 8.9744 0.0000 1.0000 Emo Wholebrain 0.1245 20.9741 0.0000 1.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.16056 0.006207 -25.867 2.2204e-15 -3.0275 {'Cog Wholebrain' } 0.030713 0.0034203 8.9797 2.2782e-13 1.051 {'Emo Wholebrain' } 0.12448 0.0058796 21.172 2.2204e-15 2.478

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {73×3 cell} text_han: {73×3 cell} point_han: {73×3 cell} star_handles: [9.0015 10.0015 11.0015]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(imgs2, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.14697 0.0061867 -23.756 2.2204e-15 -2.7805 {'Cog Wholebrain' } 0.029853 0.0031711 9.4141 3.5527e-14 1.1018 {'Emo Wholebrain' } 0.11335 0.0057763 19.624 2.2204e-15 2.2968

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {73×3 cell} text_han: {73×3 cell} point_han: {73×3 cell} star_handles: [12.0015 13.0015 14.0015]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

clear all;

close all;

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0014.nii'));

spm('Defaults','fMRI');

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel/sub-0014/con_0014.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 29951100 bytes Loading image number: 75 Elapsed time is 9.918197 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 7196241 Bit rate: 22.78 bits

contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...

'stim_P', 'stim_V', 'stim_C', 'stim_G',...

'stimXexpect_P', 'stimXexpect_V', 'stimXexpect_C',...

'motor', ...

'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...

'simple_stim_P', 'simple_stim_V', 'simple_stim_C',...

'simple_stimXexpect_P', 'simple_stimXexpect_V', 'simple_stimXexpect_C'};

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans') % display

SPM12: spm_check_registration (v7759) 19:12:15 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

ans = 1×1 cell array

{1×1 region}

drawnow; snapnow;

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 30.67% Expected 3.75 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 2 images Cases 22 72 Uncorrected: 8 images Cases 18 22 23 27 46 52 72 75 Retained 21 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 41.33% Expected 3.75 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 2 images Cases 2 67 Uncorrected: 8 images Cases 2 4 38 43 44 63 67 68 Mahalanobis (cov and corr, q<0.05 corrected): 4 images Outlier_count Percentage _____________ __________ global_mean 1 1.3333 global_mean_to_variance 2 2.6667 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 1 1.3333 mahal_cov_uncor 8 10.667 mahal_cov_corrected 2 2.6667 mahal_corr_uncor 8 10.667 mahal_corr_corrected 2 2.6667 Overall_uncorrected 16 21.333 Overall_corrected 5 6.6667

SPM12: spm_check_registration (v7759) 19:12:47 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 75

%for s = 1:length(wh_outlier_corr)

% disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 5 participants, size is now 70

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are outliers:... sub-…" "participants that are outliers:... sub-…" "participants that are outliers:... sub-…" "participants that are outliers:... sub-…" "participants that are outliers:... sub-…"

disp(n);

{'sub-0015'} {'sub-0051'} {'sub-0088'} {'sub-0123'} {'sub-0129'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 19:12:52 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.031253 Image 1 30 contig. clusters, sizes 1 to 62024 Positive effect: 42852 voxels, min p-value: 0.00000000 Negative effect: 19566 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 19:12:54 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 1575 voxels displayed, 60843 not displayed on these slices

sagittal montage: 1486 voxels displayed, 60932 not displayed on these slices

sagittal montage: 1460 voxels displayed, 60958 not displayed on these slices

axial montage: 11673 voxels displayed, 50745 not displayed on these slices

axial montage: 12686 voxels displayed, 49732 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ ____________ __________ ________________ -0.35807 {'rating' } 0.48312 {'visual' } -0.34663 {'trait' } 0.39845 {'object' } -0.3193 {'anxiety' } 0.38463 {'objects' } -0.31872 {'negative'} 0.33139 {'attention' } -0.30582 {'positive'} 0.32531 {'rotation' } -0.30271 {'disorder'} 0.3222 {'shape' } -0.29958 {'ratings' } 0.3131 {'letter' } -0.29735 {'affect' } 0.28008 {'visuospatial'} -0.29459 {'age' } 0.27754 {'orthographic'} -0.29055 {'personal'} 0.27679 {'naming' }

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(imgs2, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.1409 -23.0372 0.0000 1.0000 Cog Wholebrain 0.0232 6.2278 0.0000 1.0000 Emo Wholebrain 0.1126 18.0891 0.0000 1.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.14088 0.0060401 -23.323 2.2204e-15 -2.7877 {'Cog Wholebrain' } 0.023162 0.0037167 6.2318 3.1807e-08 0.74484 {'Emo Wholebrain' } 0.11261 0.0061816 18.217 2.2204e-15 2.1773

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {70×3 cell} text_han: {70×3 cell} point_han: {70×3 cell} star_handles: [9.0016 10.0016 11.0016]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(imgs2, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.13624 0.0059953 -22.724 2.2204e-15 -2.7161 {'Cog Wholebrain' } 0.022525 0.0035854 6.2824 2.5865e-08 0.75089 {'Emo Wholebrain' } 0.10981 0.0062431 17.59 2.2204e-15 2.1024

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {70×3 cell} text_han: {70×3 cell} point_han: {70×3 cell} star_handles: [12.0016 13.0016 14.0016]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

clear all;

close all;

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0015.nii'));

spm('Defaults','fMRI');

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel/sub-0014/con_0015.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 29951100 bytes Loading image number: 75 Elapsed time is 12.905863 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 7198220 Bit rate: 22.78 bits

contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...

'stim_P', 'stim_V', 'stim_C', 'stim_G',...

'stimXexpect_P', 'stimXexpect_V', 'stimXexpect_C',...

'motor', ...

'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...

'simple_stim_P', 'simple_stim_V', 'simple_stim_C',...

'simple_stimXexpect_P', 'simple_stimXexpect_V', 'simple_stimXexpect_C'};

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans'); % display

SPM12: spm_check_registration (v7759) 19:14:49 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

drawnow; snapnow;

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 5 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 33.33% Expected 3.75 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 2 images Cases 29 46 Uncorrected: 8 images Cases 3 4 14 29 42 46 48 72 Retained 14 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 33.33% Expected 3.75 outside 95% ellipsoid, found 7 Potential outliers based on mahalanobis distance: Bonferroni corrected: 3 images Cases 9 43 44 Uncorrected: 7 images Cases 4 9 43 44 45 63 68 Mahalanobis (cov and corr, q<0.05 corrected): 5 images Outlier_count Percentage _____________ __________ global_mean 2 2.6667 global_mean_to_variance 3 4 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 0 0 mahal_cov_uncor 8 10.667 mahal_cov_corrected 2 2.6667 mahal_corr_uncor 7 9.3333 mahal_corr_corrected 3 4 Overall_uncorrected 14 18.667 Overall_corrected 5 6.6667

SPM12: spm_check_registration (v7759) 19:15:20 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 75

%for s = 1:length(wh_outlier_corr)

% disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 5 participants, size is now 70

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are outliers:... sub-…" "participants that are outliers:... sub-…" "participants that are outliers:... sub-…" "participants that are outliers:... sub-…" "participants that are outliers:... sub-…"

disp(n);

{'sub-0028'} {'sub-0061'} {'sub-0082'} {'sub-0085'} {'sub-0088'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 19:15:26 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.032509 Image 1 26 contig. clusters, sizes 1 to 64529 Positive effect: 43010 voxels, min p-value: 0.00000000 Negative effect: 21908 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 19:15:27 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 1752 voxels displayed, 63166 not displayed on these slices

sagittal montage: 1621 voxels displayed, 63297 not displayed on these slices

sagittal montage: 1613 voxels displayed, 63305 not displayed on these slices

axial montage: 11977 voxels displayed, 52941 not displayed on these slices

axial montage: 13101 voxels displayed, 51817 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _____________ __________ _____________ -0.36182 {'trait' } 0.49668 {'visual' } -0.34604 {'rating' } 0.39022 {'object' } -0.32038 {'anxiety' } 0.37536 {'objects' } -0.31859 {'negative' } 0.3259 {'shape' } -0.30936 {'disorder' } 0.32546 {'rotation' } -0.30848 {'affect' } 0.31288 {'attention'} -0.30476 {'positive' } 0.28533 {'naming' } -0.29948 {'personal' } 0.2826 {'letter' } -0.29935 {'age' } 0.28001 {'execution'} -0.29447 {'affective'} 0.2748 {'visually' }

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(imgs2, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.1327 -23.2943 0.0000 1.0000 Cog Wholebrain 0.0282 8.4733 0.0000 1.0000 Emo Wholebrain 0.1004 17.6561 0.0000 1.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.1327 0.005614 -23.638 2.2204e-15 -2.8252 {'Cog Wholebrain' } 0.028219 0.0033297 8.4749 2.7063e-12 1.0129 {'Emo Wholebrain' } 0.10035 0.0056492 17.765 2.2204e-15 2.1233

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {70×3 cell} text_han: {70×3 cell} point_han: {70×3 cell} star_handles: [9.0017 10.0017 11.0017]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(imgs2, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.12763 0.0055127 -23.152 2.2204e-15 -2.7671 {'Cog Wholebrain' } 0.027651 0.0031356 8.8187 6.3827e-13 1.054 {'Emo Wholebrain' } 0.096844 0.0055568 17.428 2.2204e-15 2.0831

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {70×3 cell} text_han: {70×3 cell} point_han: {70×3 cell} star_handles: [12.0017 13.0017 14.0017]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

drawnow, snapnow;

% save html

pubdir = pwd;

pubfilename = 's03_PVC_cue_dummy_ROI.mlx';

p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...

'format', 'html', 'outputDir', pubdir, ...

'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));

htmlfile = publish(pubfilename, p);

Error using evalmxdom>instrumentAndRun (line 116)
Publishing a script that contains a publish function is not supported.

Error in evalmxdom (line 21)
[data,text,laste] = instrumentAndRun(file,cellBoundaries,imageDir,imagePrefix,options);

Error in publish

Dummy-coded maps for cue encoding

Pain Cue only (dummy-coded; pain cue > rest)

Pain Cue only :: load dataset

Pain Cue only :: check data coverage

Pain Cue only :: Plot diagnostics, before l2norm

Pain Cue only :: run robfit

Pain Cue only :: remove outliers based on plot

Pain Cue only :: plot diagnostics, after l2norm

Pain Cue only :: ttest

Pain Cue only :: Neurosynth similarity

Pain Cue only :: Pattern Phil

Vicarious Cue only (dummy-coded; Vicarious Cue > rest)

Vicarious Cue only :: load dataset

Vicarious Cue only :: check data coverage

Vicarious Cue only :: Plot diagnostics, before l2norm

Vicarious Cue only :: run robfit

Vicarious Cue only :: remove outliers based on plot

Vicarious Cue only :: plot diagnostics, after l2norm

Vicarious Cue only :: ttest

Vicarious Cue only :: Neurosynth similarity

Cognitive Cue only (dummy-coded; Cognitive Cue > rest)

Cognitive Cue only :: load dataset

Cognitive Cue only :: check data coverage

Cognitive Cue only :: Plot diagnostics, before l2norm

Cognitive Cue only :: run robfit

Cognitive Cue only :: remove outliers based on plot

Cognitive Cue only:: plot diagnostics, after l2norm

Cognitive Cue only :: ttest

Cognitive Cue only :: Neurosynth similarity

Cognitive Cue only :: Pattern Phil